Weekly Exercises #3

library(tidyverse)     # for graphing and data cleaning
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
library(geofacet)      # for special faceting with US map layout
theme_set(theme_minimal())       # My favorite ggplot() theme :)

# Lisa's garden data
data("garden_harvest")

# Seeds/plants (and other garden supply) costs
data("garden_spending")

# Planting dates and locations
data("garden_planting")

# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Setting up on GitHub!

Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 3rd weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises with garden data

These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

1. Summarize the garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.

garden_harvest %>% 
  mutate(day = wday(date, label = TRUE)) %>% 
  group_by(vegetable, day) %>% 
  summarise(total_weight = sum(weight)) %>% 
  pivot_wider(id_cols = vegetable:total_weight,
              names_from = "day",
              values_from = "total_weight")

2. Summarize the garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?

garden_harvest %>% 
  group_by(variety) %>% 
  summarise(total_weight_lbs = sum(weight * 0.00220462)) %>% 
  left_join(garden_planting, 
            by = "variety") %>% 
  select(variety:plot)

Some of the vegetable varieties show up twice, with the same total weight. If each variety was weighed and recorded with their plot this could help to solve this issue. We could also average the weight out proportionally based on the number of seeds planted in each plot for each variety.

3. I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.

Using the garden harvest dataset as the record of vegetables harvested and their weights, we could join this with the garden spending dataset by vegetable and variety to find the seed price for each variety. The price of the top soil and dirt would be lost in this join process but this could be added later as a constant cost for each vegetable. With the Whole Foods dataset we could perform another join function, this time just by vegetable. Now we would have a dataset with the weight of each vegetable, the seed price and the price of it in a store. We could then group this data by vegetable and create two new variables, the total seed price and the total cost of it at the store (adjusted for weight). With these new variables we could create a third variable for each vegetable, money saved, which is the seed price - store price.

4. Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.

garden_harvest %>% 
  filter(vegetable == "tomatoes") %>% 
  group_by(variety) %>% 
  summarise(total_weight_lbs = sum(weight * 0.00220462),
            first_harvest_date = min(date)) %>% 
  arrange(first_harvest_date) %>% 
  ggplot(mapping = aes(x = total_weight_lbs, y = fct_reorder(variety, first_harvest_date, .desc = TRUE))) +
  geom_col() +
  labs(title = "Tomato harvest weights by variety, ordered by harvest date",
       x = "",
       y = "")

5. In the garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().

garden_harvest %>% 
  mutate(lower_variety = str_to_lower(variety),
         length_variety = str_length(lower_variety)) %>% 
  distinct(lower_variety, .keep_all = TRUE) %>% 
  arrange(vegetable, length_variety)

6. In the garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().

garden_harvest %>% 
  distinct(variety, .keep_all = TRUE) %>% 
  group_by(vegetable) %>% 
  filter(str_detect(variety, "er|ar"))

Bicycle-Use Patterns

In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.

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Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usually, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.

Temporal patterns

It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:

7. A density plot, which is a smoothed out histogram, of the events versus sdate. Use geom_density().

options(scipen = 10)
Trips %>% 
  ggplot(mapping = aes(x = sdate)) +
  geom_density() +
  labs(title = "Number of bike rentals by date",
       x = "Date",
       y = "Density")

As the months get colder, the number of rentals decrease. There is an increase in the early part of December before decreasing again in January.

8. A density plot of the events versus time of day. You can use mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60) %>% 
  ggplot(mapping = aes(x = time_day)) +
  geom_density() +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day",
       x = "Time of Day",
       y = "Density")

There are two rental peaks, one around 9:00AM and another around 5:30PM. This could mean that people are using the bikes to commute. In the middle of the day there is a lull, but bike usage is still higher than other parts of the day, such as the early morning or late evening.

9. A bar graph of the events versus day of the week. Put day on the y-axis.

Trips %>% 
  mutate(day = wday(sdate, label = TRUE)) %>% 
  ggplot(mapping = aes(y = day)) +
  geom_bar() +
  scale_y_discrete(limits = c("Sat", "Fri", "Thu", 
"Wed", "Tue", "Mon", "Sun")) +
  labs(title = "Bike rentals by day of the week",
       x = "",
       y= "")

This plot adds further evidence from my previous hypothesis that people are potentially using the bikes to commute. This is because during the week days, there are more bicycle rentals than during the weekends.

10. Facet your graph from exercise 8. by day of the week. Is there a pattern?

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60,
         day = wday(sdate, label = TRUE)) %>% 
  ggplot(mapping = aes(x = time_day)) +
  geom_density() +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day, separated by day of the week",
       x = "Time of Day",
       y = "Density") +
  facet_wrap(vars(day))

There is a noticeable pattern. On weekdays, people are using the bikes to commute as evidenced by the two peaks, one at the start of the work day and another at the end. On the weekends, people are riding the bikes in the early to late afternoon.

The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.

11. Change the graph from exercise 10 to set the fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60,
         day = wday(sdate, label = TRUE)) %>% 
  ggplot(mapping = aes(x = time_day, fill = client)) +
  geom_density(color = NA, alpha = .5) +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day, separated by day of the week",
       x = "Time of Day",
       y = "Density",
       fill = "Client Type") +
  facet_wrap(vars(day))

It appears as though registered riders are more likely to use the bikes for their commute, at the beginning or end of the work day. Casual riders are more likely to use the bikes in the middle of the day and during weekends.

12. Change the previous graph by adding the argument position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60,
         day = wday(sdate, label = TRUE)) %>% 
  ggplot(mapping = aes(x = time_day, fill = client)) +
  geom_density(color = NA, alpha = .5, position = position_stack()) +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day, separated by day of the week",
       x = "Time of Day",
       y = "Density",
       fill = "Client Type") +
  facet_wrap(vars(day))

In my opinion, this is worse for telling a story. It makes it harder to see where each type of rider is using the bikes. At best it tells us which are the most common times for bike rentals overall. In terms of the story I was trying to tell with commuters, this version of the plot doesn’t help. It makes it appear that casual and registered riders use the bikes at similar times.

13. In this graph, go back to using the regular density plot (without position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60,
         day = wday(sdate, label = TRUE),
         weekend = ifelse(day == "Sat" | day == "Sun", "Weekend", "Weekday")) %>% 
  ggplot(mapping = aes(x = time_day, fill = client)) +
  geom_density(color = NA, alpha = .5) +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day, separated by weekday or weekend",
       x = "Time of Day",
       y = "Density",
       fill = "Client Type") +
  facet_wrap(vars(weekend))

Similar to the other plots, this shows that there is a clear difference in when the types of riders use the bikes, casual riders during the middle part of the day and registered riders during their commute. Interestingly, there are a group of registered riders that are using the bikes in the early hours of the weekend mornings.

14. Change the graph from the previous problem to facet on client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?

options(scipen = 10)
Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time_day = hour + minute/60,
         day = wday(sdate, label = TRUE),
         weekend = ifelse(day == "Sat" | day == "Sun", "Weekend", "Weekday")) %>% 
  ggplot(mapping = aes(x = time_day, fill = weekend)) +
  geom_density(color = NA, alpha = .5) +
  xlim(0, 24)+
  labs(title = "Number of bike rentals by time of day, separated by client type",
       x = "Time of Day",
       y = "Density",
       fill = "") +
  facet_wrap(vars(client))

It appears that this tells us a very similar story to the previous graphs. It makes it easier to compare trends within each type of client. Casual riders use the bikes at similar times but more frequently on the weekends. Registered riders use the bikes at completely different times on the weekends, usually during the middle of the day. I think these two graphs are both useful, it just depends what story you are trying to tell.

Spatial patterns

15. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!

Trips_Station <- Trips
names(Trips_Station)[names(Trips_Station) == "sstation"] <- "name"
Trips_Station %>% 
  group_by(name) %>% 
  summarise(num_departures = n()) %>% 
  left_join(Stations, by = "name") %>% 
  ggplot(mapping = aes(x = lat, y = long, color = num_departures)) + 
  geom_point() +
  labs(title = "Number of departures from each station",
       x = "",
       y = "",
       color = "Number of Departures") +
  scale_color_viridis_c()

16. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? (Again, we’ll improve this next week when we learn about maps).

Trips_Station %>% 
  group_by(name, client) %>% 
  summarise(num_departures = n()) %>% 
  mutate(total_departures = sum(num_departures),
         percent_casual = ifelse(client == "Casual", num_departures/total_departures, 0)) %>% 
  filter(percent_casual != 0) %>% 
left_join(Stations, by = "name") %>% 
  ggplot(mapping = aes(x = lat, y = long, color = percent_casual)) + 
  geom_point() +
  labs(title = "Number of departures from each station",
       x = "",
       y = "",
       color = "Number of Departures") +
  scale_color_viridis_c()

The casual users have higher percentages of bicycle usage in the stations that are further out. They have lower percentages in the stations that are clustered together in the upper left of the plot.

Spatiotemporal patterns

17. Make a table with the ten station-date combinations (e.g., 14th & V St., 2014-10-14) with the highest number of departures, sorted from most departures to fewest. Save this to a new dataset and print out the dataset. Hint: as_date(sdate) converts sdate from date-time format to date format.

top_ten_station_data <- Trips %>% 
  mutate(date = as_date(sdate)) %>% 
  group_by(sstation, date) %>% 
  summarise(num_departures = n()) %>% 
  arrange(desc(num_departures)) %>% 
  head(10)

top_ten_station_data

18. Use a join operation to make a table with only those trips whose departures match those top ten station-date combinations from the previous part.

Trips %>%
  mutate(date = as_date(sdate)) %>% 
  semi_join(top_ten_station_data,
            by = c("sstation", "date"))

19. Build on the code from the previous problem (ie. copy that code below and then %>% into the next step.) and group the trips by client type and day of the week (use the name, not the number). Find the proportion of trips by day within each client type (ie. the proportions for all 7 days within each client type add up to 1). Display your results so day of week is a column and there is a column for each client type. Interpret your results.

Trips %>%
  mutate(date = as_date(sdate),
         dow = wday(date, label = TRUE)) %>% 
  semi_join(top_ten_station_data,
            by = c("sstation", "date")) %>% 
  group_by(client, dow) %>%
  summarise(num_departures = n()) %>% 
  mutate(total_departures = sum(num_departures)) %>% 
  group_by(client, dow) %>%
  summarise(prop = num_departures/total_departures) %>% 
  pivot_wider(id_cols = client:prop,
              names_from = "client",
              values_from = "prop")

Of the top 10 days and station combinations, none of these days are Fridays. The vast majority of the casual rides happen on the weekend, with over half coming on Saturday. The opposite is true of the registered riders, with the majority coming during the week. A large majority of the registered riders rides come on Thursday. This fits in with the story told by the rest of the plots, registered riders are using the bikes to commute and casual riders use them to get around on the weekends.

DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.

GitHub link

20. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 03_exercises.Rmd, provide a link to the 03_exercises.md file, which is the one that will be most readable on GitHub.

https://github.com/abmhopkins/STAT112_Exercise_03/blob/main/03_exercises.md

(the table outputs in the markdown file are tough to read)

Challenge problem!

This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.

21. In this exercise, you are going to try to replicate the graph below, created by Georgios Karamanis. I’m sure you can find the exact code on GitHub somewhere, but DON’T DO THAT! You will only be graded for putting an effort into this problem. So, give it a try and see how far you can get without doing too much googling. HINT: use facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.

kids %>% 
  filter(variable == "lib",
         year == 1997 | year == 2016) %>% 
  select(state, year, inf_adj_perchild) %>% 
  pivot_wider(id_cols = state:inf_adj_perchild,
              names_from = "year",
              values_from = "inf_adj_perchild") %>% 
  mutate(increase = ifelse(`2016`>`1997`, TRUE, FALSE)) %>% 
  
  ggplot() +
  geom_point(aes(x = 0, y = `1997`*1000, color = ifelse(increase == TRUE, "white", "black")), size = 1.5) +
  
  geom_segment(aes(x = 0,
                   xend = 1,
                   y = `1997`*1000,
                   yend = `2016`*1000,
                   color = ifelse(increase == TRUE, "white", "black")), 
                arrow = arrow(length = unit(0.075, "inches")),
                size = 0.8) +
  
  geom_text(aes(x = 0,
                y = `1997`-200,
                label = round(`1997`*1000)),
                hjust = 0.5,
                size = 3,
                color = "grey") +
  
   geom_text(aes(x = 1.05,
                y = `2016`-200,
                label = round(`2016`*1000)),
                hjust = 0.5,
                size = 3,
                color = "grey") +
  
  scale_color_identity()+
  
  facet_geo(vars(state), label ="code") +
  
  theme(plot.background = element_rect(fill = "#6C8299"),
        panel.grid = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        plot.title = element_text(face = "bold", hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        plot.margin = margin(5,20,5,20),
        panel.spacing.y = unit(0.75, "lines"),
        panel.spacing.x = unit(1.25 , "lines"),
        strip.text = element_text(color = "white"),
        legend.position = "none") +
  
    coord_cartesian(clip = "off", expand = FALSE) +
  
  labs(title = "Change in public spending on libraries from 1997 to 2016",
       subtitle = "Dollars spent per child, adjusted for inflation",
       x = "",
       y = "",
       caption = "Source: Urban Institute | Inspired by: Georgios Karamanis | Graphic: Alexander Hopkins")

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?